The present application relates generally to an improved data processing system and method and more specifically to mechanisms for leveraging an external ontology for graph expansion in inference systems.
With the increased usage of computing networks, such as the Internet, humans are currently inundated and overwhelmed with the amount of information available to them from various structured and unstructured sources. However, information gaps abound as users try to piece together what they can find that they believe to be relevant during searches for information on various subjects. To assist with such searches, recent research has been directed to generating question and answer (QA) systems which may take an input question, analyze it, and return results indicative of the most probable answer to the input question. QA systems provide automated mechanisms for searching through large sets of sources of content, e.g., electronic documents, and analyze them with regard to an input question to determine candidate answers to the question and confidence measures representing probabilities that a candidate answer correctly answers the input question.
The IBM Watson™ system is an application of advanced natural language processing, information retrieval, knowledge representation and reasoning, and machine learning technologies to the field of open domain question answering. The IBM Watson™ system is built on IBM's DecpQA technology used for hypothesis generation, massive evidence gathering, analysis, and scoring. IBM Watson™ takes an input question, analyzes it, decomposes the question into constituent parts, generates one or more hypothesis based on the decomposed question and results of a primary search of answer sources, performs hypothesis and evidence scoring based on a retrieval of evidence from evidence sources, performs synthesis of the one or more hypothesis, and based on trained models, performs a final merging and ranking to output an answer to the input question along with a confidence measure.
WatsonPaths is an exploratory system from IBM Research that builds on the IBM Watson™ question answering system. WatsonPaths breaks down an input scenario into individual pieces of information, asks relevant sub-questions of IBM Watson™ to conclude new information, and represents these results in a graphical model. Probabilistic inference is performed over the graph to conclude an answer.